Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

[This corrects the article DOI: 10.3389/fncom.2023.1108889.].

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.

Scanning Compton X-ray microscopy

X-ray microscopy offers the opportunity to image biological and radiosensitive materials without special sample preparations, bridging optical and electron microscopy capabilities. However, the performance of such microscopes, when imaging radiosensitive samples, is not limited by their intrinsic resolution, but by the radiation damage induced on such samples. Here, we demonstrate a novel, to the best of our knowledge, radio-efficient microscope, scanning Compton X-ray microscopy (SCXM), which uses coherently and incoherently (Compton) scattered photons to minimize the deposited energy per unit of mass for a given imaging signal. We implemented SCXM, using lenses capable of efficiently focusing 60 keV X-ray photons into the sub-micrometer scale, and probe its radio-efficient capabilities. SCXM, when implemented in high-energy diffraction-limited storage rings, e.g., European Synchrotron Radiation Facility Extremely Brilliant Source and PETRA IV, will open the opportunity to explore the nanoscale of unstained, unsectioned, and undamaged radiosensitive materials.

Accumulation of amino acids and glucose in isolated epitheliocytes from the small intestine of chickens depending on both breed and sex

In an experiment with chickens breed differences were established in accumulation of 14C-lysine, methionine and glycine, as well as intra-line differences in glycine accumulation in intestinal epitheliocytes from Plymouth Rock chickens. Differences were found in both methionine and glycine accumulation, between B line and four-line hybrids as well. Methionine accumulation in epitheliocytes from the small intestine of male chickens was higher than in female chickens from A line, higher than accumulation of tryptophane in both A and C lines and accumulation of glucose in D line. In male chickens of four-line hybrid, glycine accumulation was higher than in females.

Isolation of plasma membranes from ram spermatozoa by a two-phase polymer system

The plasma membranes of ram spermatozoa were disrupted in a hypotonic EDTA medium and isolated by using a two-phase polymer system of dextran--polyethyleneglycol. The plasma membranes obtained were of a relatively high degree of purity (approximately 70%) as judged by electron microscopy observations and measurements of the marker enzymes alkaline phosphatase, ATPase and AMPase. The activity of succinate cytochrome C reductase, a marker of mitochondrial membranes, was very low.